11 citations · 24 across the 10 of their papers we have counts for
11 papers
Breaking the Loop: Detecting and Mitigating Denial-of-Service Vulnerabilities in Large Language Models
Junzhe Yu, Yi Liu, Huijia Sun +2
Large Language Models (LLMs) have significantly advanced text understanding and generation, becoming integral to applications across education, software development, healthcare, en…
Efficient Detection of Toxic Prompts in Large Language Models
Yi Liu, Junzhe Yu, Huijia Sun +4
Large language models (LLMs) like ChatGPT and Gemini have significantly advanced natural language processing, enabling various applications such as chatbots and automated content g…
Continuous Embedding Attacks via Clipped Inputs in Jailbreaking Large Language Models
Zihao Xu, Yi Liu, Gelei Deng +4
Security concerns for large language models (LLMs) have recently escalated, focusing on thwarting jailbreaking attempts in discrete prompts. However, the exploration of jailbreak v…
DistillSeq: A Framework for Safety Alignment Testing in Large Language Models using Knowledge Distillation
Mingke Yang, Yuqi Chen, Yi Liu +1
Large Language Models (LLMs) have showcased their remarkable capabilities in diverse domains, encompassing natural language understanding, translation, and even code generation. Th…
Self and Cross-Model Distillation for LLMs: Effective Methods for Refusal Pattern Alignment
Jie Li, Yi Liu, Chongyang Liu +4
Large Language Models (LLMs) like OpenAI's GPT series, Anthropic's Claude, and Meta's LLaMa have shown remarkable capabilities in text generation. However, their susceptibility to…
Uncovering Logit Suppression Vulnerabilities in LLM Safety Alignment
Yuxi Li, Yi Liu, Yuekang Li +4
Large language models (LLMs) have revolutionized various applications, making robust safety alignment essential to prevent harmful outputs. Current safety alignment techniques, how…
Drowzee: Metamorphic Testing for Fact-Conflicting Hallucination Detection in Large Language Models
Ningke Li, Yuekang Li, Yi Liu +3
Large language models (LLMs) have transformed the landscape of language processing, yet struggle with significant challenges in terms of security, privacy, and the generation of se…
Glitch Tokens in Large Language Models: Categorization Taxonomy and Effective Detection
Yuxi Li, Yi Liu, Gelei Deng +7
With the expanding application of Large Language Models (LLMs) in various domains, it becomes imperative to comprehensively investigate their unforeseen behaviors and consequent ou…
Groot: Adversarial Testing for Generative Text-to-Image Models with Tree-based Semantic Transformation
Yi Liu, Guowei Yang, Gelei Deng +5
With the prevalence of text-to-image generative models, their safety becomes a critical concern. adversarial testing techniques have been developed to probe whether such models can…
A Cross-Language Investigation into Jailbreak Attacks in Large Language Models
Jie Li, Yi Liu, Chongyang Liu +5
Large Language Models (LLMs) have become increasingly popular for their advanced text generation capabilities across various domains. However, like any software, they face security…
MiniScope: Automated UI Exploration and Privacy Inconsistency Detection of MiniApps via Two-phase Iterative Hybrid Analysis
Shenao Wang, Yuekang Li, Kailong Wang +4
The advent of MiniApps, operating within larger SuperApps, has revolutionized user experiences by offering a wide range of services without the need for individual app downloads. H…